


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
					import torch


if is_vision_available():
					from PIL import Image

					from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				ViTImageProcessor if is_vision_available() else None

				@property
				def 	a__		(     self							)    ->  Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		(3, 3_2, 1_2_8)
					lowercase						:		Tuple   					=		tempfile.mkdtemp()

					# fmt: off
					lowercase						:		Union[str, Any]   					=		["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
					# fmt: on
					lowercase						:		Union[str, Any]   					=		dict(zip(SCREAMING_SNAKE_CASE_							,      range(len(SCREAMING_SNAKE_CASE_							)							)							)							)

					lowercase						:		List[Any]   					=		os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES["vocab_file"]							)
					with open(self.vocab_file							,      "w"							,      encoding="utf-8"							) as fp:
						fp.write(json.dumps(SCREAMING_SNAKE_CASE_							) + "\n"							)

					lowercase						:		Tuple   					=		{
					    "do_normalize": False,
					    "do_resize": True,
					    "image_processor_type": "ViTImageProcessor",
					    "resample": 3,
					    "size": {"height": 3_2, "width": 1_2_8},
					}
					lowercase						:		str   					=		os.path.join(self.tmpdirname							,      SCREAMING_SNAKE_CASE_							)
					with open(self.image_processor_file							,      "w"							,      encoding="utf-8"							) as fp:
						json.dump(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							,      **a_							)    ->  Tuple:
					return MgpstrTokenizer.from_pretrained(self.tmpdirname							,      **SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							,      **a_							)    ->  str:
					return ViTImageProcessor.from_pretrained(self.tmpdirname							,      **SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  Dict:
					shutil.rmtree(self.tmpdirname							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Any   					=		np.random.randint(2_5_5							,      size=(3, 3_0, 4_0_0)							,      dtype=np.uinta							)

					lowercase						:		Union[str, Any]   					=		Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_							,      0							,      -1							)							)

					return image_input

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Tuple   					=		self.get_tokenizer()
					lowercase						:		int   					=		self.get_image_processor()

					lowercase						:		int   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)
					processor.save_pretrained(self.tmpdirname							)
					lowercase						:		Optional[int]   					=		MgpstrProcessor.from_pretrained(self.tmpdirname							,      use_fast=SCREAMING_SNAKE_CASE_							)

					self.assertEqual(processor.char_tokenizer.get_vocab()							,      tokenizer.get_vocab()							)
					self.assertIsInstance(processor.char_tokenizer							,      SCREAMING_SNAKE_CASE_							)

					self.assertEqual(processor.image_processor.to_json_string()							,      image_processor.to_json_string()							)
					self.assertIsInstance(processor.image_processor							,      SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		List[Any]   					=		self.get_tokenizer()
					lowercase						:		int   					=		self.get_image_processor()

					lowercase						:		Any   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)
					processor.save_pretrained(self.tmpdirname							)

					lowercase						:		Optional[int]   					=		self.get_tokenizer(bos_token="(BOS)"							,      eos_token="(EOS)"							)
					lowercase						:		int   					=		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_							,      padding_value=1.0							)

					lowercase						:		Optional[int]   					=		MgpstrProcessor.from_pretrained(
					    self.tmpdirname							,      bos_token="(BOS)"							,      eos_token="(EOS)"							,      do_normalize=SCREAMING_SNAKE_CASE_							,      padding_value=1.0							)

					self.assertEqual(processor.char_tokenizer.get_vocab()							,      tokenizer_add_kwargs.get_vocab()							)
					self.assertIsInstance(processor.char_tokenizer							,      SCREAMING_SNAKE_CASE_							)

					self.assertEqual(processor.image_processor.to_json_string()							,      image_processor_add_kwargs.to_json_string()							)
					self.assertIsInstance(processor.image_processor							,      SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		self.get_image_processor()
					lowercase						:		Union[str, Any]   					=		self.get_tokenizer()

					lowercase						:		Any   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)

					lowercase						:		Dict   					=		self.prepare_image_inputs()

					lowercase						:		str   					=		image_processor(SCREAMING_SNAKE_CASE_							,      return_tensors="np"							)
					lowercase						:		Union[str, Any]   					=		processor(images=SCREAMING_SNAKE_CASE_							,      return_tensors="np"							)

					for key in input_image_proc.keys():
						self.assertAlmostEqual(input_image_proc[key].sum()							,      input_processor[key].sum()							,      delta=1e-2							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Any   					=		self.get_image_processor()
					lowercase						:		List[Any]   					=		self.get_tokenizer()

					lowercase						:		Any   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)

					lowercase						:		List[str]   					=		"test"

					lowercase						:		int   					=		processor(text=SCREAMING_SNAKE_CASE_							)

					lowercase						:		Optional[Any]   					=		tokenizer(SCREAMING_SNAKE_CASE_							)
					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]							,      encoded_processor[key]							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		Optional[int]   					=		self.get_image_processor()
					lowercase						:		List[Any]   					=		self.get_tokenizer()

					lowercase						:		List[str]   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)

					lowercase						:		Any   					=		"test"
					lowercase						:		Union[str, Any]   					=		self.prepare_image_inputs()

					lowercase						:		str   					=		processor(text=SCREAMING_SNAKE_CASE_							,      images=SCREAMING_SNAKE_CASE_							)

					self.assertListEqual(list(inputs.keys()							)							,      ["pixel_values", "labels"]							)

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE_							):
						processor()

				def 	a__		(     self							)    ->  Any:
					lowercase						:		int   					=		self.get_image_processor()
					lowercase						:		List[str]   					=		self.get_tokenizer()

					lowercase						:		Optional[Any]   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)

					lowercase						:		List[Any]   					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					lowercase						:		int   					=		processor.char_decode(SCREAMING_SNAKE_CASE_							)
					lowercase						:		Optional[Any]   					=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_							)
					lowercase						:		Optional[Any]   					=		[seq.replace(" "							,      ""							) for seq in decoded_tok]

					self.assertListEqual(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		Optional[int]   					=		self.get_image_processor()
					lowercase						:		List[Any]   					=		self.get_tokenizer()

					lowercase						:		str   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)

					lowercase						:		int   					=		None
					lowercase						:		Tuple   					=		self.prepare_image_inputs()

					lowercase						:		Dict   					=		processor(text=SCREAMING_SNAKE_CASE_							,      images=SCREAMING_SNAKE_CASE_							)

					self.assertListEqual(list(inputs.keys()							)							,      processor.model_input_names							)


				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		self.get_image_processor()
					lowercase						:		List[Any]   					=		self.get_tokenizer()

					lowercase						:		int   					=		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,      image_processor=SCREAMING_SNAKE_CASE_							)

					lowercase						:		Optional[Any]   					=		torch.randn(1							,      2_7							,      3_8							)
					lowercase						:		List[str]   					=		torch.randn(1							,      2_7							,      5_0_2_5_7							)
					lowercase						:		List[Any]   					=		torch.randn(1							,      2_7							,      3_0_5_2_2							)

					lowercase						:		Optional[Any]   					=		processor.batch_decode([char_input, bpe_input, wp_input]							)

					self.assertListEqual(list(results.keys()							)							,      ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]							)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	Dict      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


